What's New About the New Economy? IT, Economic Growth and Productivity
Why this work is in the frame
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Bibliographic record
Abstract
The U.S. economy performed extraordinarily well in the 1990s. Unemployment has dropped to historically low rates; the federal government is awash with revenues, and after a quarter century of near stagnation, productivity growth is soaring. The unexpected economic strength has stimulated much discussion about the ‘new economy, ’ and what the emergence of a new economy implies for the sustainability of the economic expansion in future years. The ‘new economy ’ discussion has been inconclusive, in part because the term ‘new economy ’ means different things to different people.1 Some definitions of the new economy embrace a very broad notion—that the fundamental economic concepts that guided economic policy in the past have become irrelevant in an age of global competition and rapid technological change. Others have a more narrow focus—the role of information processing and communications technology (IT) in accelerating the economy’s trend rate of output and productivity growth In this paper, we address primarily the narrower focus. New technologies are a fundamental part of the new economy notion, even if they represent only part of what some commentators mean by the term. OECD (2000) remarks that “something fundamental has changed ” in the U.S. economy, and Nezu (2000), presumably voicing the views of his OECD 1 Cohen, DeLong, and Zysman (2000) opt for the term “e-economy, ” because, as they observe: “the term ‘new economy ’ is too broad: it can carry (and has carried) anything anybody wants to put into it. ” In their view, the e-economy is “driven by the development and diffusion of modern electronics-based information technology.” 2collaborators, says that “most people agree that…information and communication technology, or IT, lies at its heart. ” One major source of contention revolves around the question of whether the economic effects of the new technologies embodied in IT are captured by conventional, or ‘old’, economic concepts and analysis. We contend that they are, and that the impact of IT is not so much “new ” as it is larger than before.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it